Professor, Electrical and Computer Engineering
Elias Masry joined the UCSD faculty in July 1968, after receiving his Ph.D. in Electrical Engineering the same year from Princeton University. He received B.Sc. and M.Sc. degrees in Electrical Engineering from the Technion-Israel Institute of Technology, in 1963 and 1965 respectively, and an M.S. in Electrical Engineering from Princeton in 1966. He was elected a Fellow of IEEE in 1986, and served as Associate Editor for Stochastic Processes for the IEEE Transactions on Information Theory 1980-83. He was the Publications Chairman of the 1990 International Symposium on Information Theory in San Diego. He was honored by his students in Electrical & Computer Engineering with the Graduate Teaching Award in 2000 and the undergraduate Teacher of The Year Award in 2001. He is a member of the Communications group at UCSD.
Professor Masry's work covers a wide range of topics in communication systems, signal processing, and mathematical statistics: He made extensive contributions in the areas of covariance, spectral, and probability density estimation, inverse problems in nonlinear systems, optimal sampling designs and Monte Carlo integration, deconvolution methods in probability density and regression functions estimation, local polynomial regression fitting for short-range and long-range dependent data, wavelet representation of stochastic processes and applications to function estimation, estimation and identification of stationary nonlinear ARCH and ARX systems, sampling theorems for stochastic processes, interference rejection in spread-spectrum communication systems, and analysis of adaptive filtering algorithms. Current research interests include multivariate curve estimation using wavelet bases, local polynomial regression fitting for short-range and long-range dependent data, estimation and identification of nonlinear time series, and analysis of adaptive filtering algorithms for communication systems. His projects include a CoRe-funded to study on 'Space-Time Processing for Wireless.' with focus on blind adaptive algorithms for channel equalization and multiuser detection.